SDS 023 : Data in Marketing, Statistical Significance and Management and Career Advice with Sam Flegal

If marketing is a career you are interested in or already work in, you will want to tune in to today's episode and hear Sam Flegal talk about his marketing career at Ontraport.

On top of how he uses data in his role, we also discuss statistical significance in depth and its importance in the field of data and marketing. As an added bonus, Sam generously shares loads of insider career tips, people management ideas and insights into the tools he finds invaluable in his daily work.

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Episode Transcript

Full Podcast Transcript

Welcome to the SuperDataScience podcast. My name is Kirill Eremenko, data science coach and lifestyle entrepreneur. And each week we bring you inspiring people and ideas to help you build your successful career in data science. Thanks for being here today and now let’s make the complex simple.

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Hello and welcome to the SuperDataScience podcast. Today we've got a very cool episode, and if you're in marketing, or you deal with marketing data in any way, shape, or form, then listen up, because you're in for a treat.

Today we've got Sam Flegal joining us, who is the marketing manager for Ontraport. Now Ontraport is a company that provides business automation software to small businesses and entrepreneurs, and it's a huge company located in Santa Barbara, California. And as you can imagine, they deal with data all the time.

So Sam is in the space of marketing, and he actually analyzes a lot of marketing data on a daily basis. And in this podcast, we're going to talk about a lot of interesting techniques that uses. So we'll talk about setting up data points, we'll talk about cutting down your data instead of analyzing everything and trying to boil the ocean. We'll talk about how he manages a team of people working in the space of analyzing data for marketing. We'll talk about how he goes about reporting data, how he creates these cool infographics to help management understand data much better, which I found really, really cool. And also, Sam will share a lot of different hacks, tools, and tips that he uses.

Plus, Sam's been at Ontraport for many years now, and he will tell us, he will reveal how his career progressed through the years. So if you are looking for a career in marketing and data, then this will be a very, very valuable discussion for you as well.

So we've got a very dynamic podcast filled with value all around coming up. And I can't wait for you to get started. So without further ado, I bring to you Sam Flegal of Ontraport.

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Hello everybody, welcome to the SuperDataScience podcast. Today with me on the show I have Sam Flegal, who is a marketing expert at Ontraport. Sam, how are you today?

Sam: I'm doing great. How are you?

Kirill: I'm good as well, thank you. Where are you calling in from?

Sam: I'm calling in from Santa Barbara, California.

Kirill: Wow, that is far away! So for those of us who don't know, I'm in Brisbane, Australia. I actually was in Santa Barbara earlier this year, and it's a 12 hour flight to LA. So well away from here. How is everything going there in Santa Barbara?

Sam: You know, things are good. It's winter here, which means that it's just barely not boiling hot all the time. So that's kinda nice. I get to wear a sweater once in a while, you know. Which is rare for me, but!

Kirill: Yeah, yeah, I can imagine. And climate there is very, very interesting. Well, I know about the -- when I was in Santa Barbara, I liked the nature. So you've got the mountains on one side, and the beautiful ocean right there. So it's something you don't even get in LA. But would you say the climate is a bit softer than the rest of California?

Sam: Oh, yeah. I mean, I’m complaining about it but the truth is it’s like a perfect 72 degrees here all the time. Most people love it. I just so happen to like colder weather so I don’t really get a lot of that but, you know, that’s personal preference.

Kirill: Yeah, totally. So just a quick brief description of how we know each other for our listeners. Sam works for a company called Ontraport, which is a fantastic company in the space of business automation, so if there’s any entrepreneurs listening out there, then definitely check them out. They provide something much more powerful than just a way to build your email list and maintain your email list. They provide a whole database and lots of automation tools. I was doing a lot of training with Ontraport this whole year. I was in Santa Barbara like over four times. Throughout my journey with Ontraport, I’ve met a lot of people that worked there, fantastic people, and Sam is actually the marketing manager. He uses a lot of data in his decision making, so that’s what we’re going to be talking about today. So just quickly to get us all up to speed, Sam, what exactly do you do as a marketing manager at Ontraport?

Sam: Yeah, so as a marketing manager here, what that essentially means is I take credit for everything else my team does. I put data into pretty graphs so that executives know why they’re paying me. That’s essentially what I do. (Laughs) In more serious terms, though, what I actually do is I manage a team of about eight people, which make up our marketing team. Together, we’re responsible for all of the marketing efforts of the company. So that includes things like email marketing funnels, advertising, e-book, you know, lead generation stuff, and sort of everything in-between. We write all this stuff, we publish all this stuff, and then me specifically—we report on the stuff and prove whether or not it’s making us money essentially. So that’s sort of what the team does and then on an individual basis, I make sure everyone is aligned around our goals, which is pretty easy to do because our executives are pretty transparent about that, as I’m sure you know because you’ve met Landon and Lena. So that’s one of my jobs. And then I’m ultimately responsible for the reporting side of everything. I take the results of all of our campaigns; like I said, I put them into pretty graphs and I then send them to Landon and Lena, our president and CEO, to tell me whether or not we should keep doing them.

Kirill: Okay, gotcha. Wow, it sounds like a very involved role. You work on both sides. You’re working with a team of people, putting it together, and talking to executives. So you’re kind of like the bridge between the analytics and the findings and the people that make business decisions. Would you agree that that’s an accurate description?

Sam: Yeah, I mean, I think that’s actually why I was put into this role, is to be sort of that bridge between our executive team and the marketing team, essentially. Back in the day when we were a lot smaller, you know, I was actually the seventh employee here at Ontraport. And when we were that small, I’d talk to Landon, our CEO, every single day about what we wanted to do and what was relevant at the time. But as we’ve grown, you know, we’re like a hundred people now, not everyone gets to have that voice from Landon and Lena about where we’re going and what we’re doing. So that’s a big part of my job. But here at Ontraport, one of our philosophies is that we do have working managers. What essentially that means is I spend about half my time doing the role of marketing and analytics and running data and all that stuff, and half of my time managing people and helping them in their careers and to be successful in their own jobs.

Kirill: Wow, that’s very interesting. So yeah, I remember Lena talking about that in one of our training sessions. Yeah, it must be quite hard, because you have to spend at least about a good hour per week with every one of the people working on your team. What does that leave you? Like, four full days of work that you can actually put in yourself.

Sam: (Laughs) It’s actually a lot less than that. And I hope Lena doesn’t listen to this, because she might construe this as me complaining, but the truth is—like you alluded to there—we do one-on-one meetings with all of our team members. So I sit down with my ad buyer Ben, I sit down with my assistant marketing manager Megan, one-on-one every single week. Then they have a tool we use, 15Five—I know it’s data science and analytics, but if anybody listening to this is doing management stuff, check out 15Five. It’s an app that allows people to fill out these like weekly reports that let executives as well as managers know if they’re happy, what roadblocks they have, things like that. So I also review all those every week. We have manager meetings, we have peer groups. I mean, there’s a ton of stuff that we do to help nurture our people around here.
And to be completely honest with you, it’s probably the part of my job that I take the most seriously. Which is odd, considering I’m responsible for the growth of the company as well, but I think that Landon and Lena would agree that nurturing our people to do their best work is more important.

Kirill: Okay. Yeah, I totally agree with that. Those listening out there, in my view personally, Ontraport is the model as a business to copy and model the way you actually run your business. They’ve got everything down pat; you know, the operation side of things, the way employees grow, the way these 15Fives are implemented and even schedules, employee schedules. Is it just in the engineering team, or is it in your team as well, where you have like one hour per day where there’s like absolute silence where nobody’s allowed to—?

Sam: That’s actually the whole company. Everywhere in the company, except for our support team because they have to be on the phones helping clients.

Kirill: (Laughs) Naturally.

Sam: But everywhere in the company, we actually have 3 hours a day: we have an hour and a half in the morning and an hour and a half in the afternoon where we have time set aside for sort of like company improvement projects. So what we found in our early days was that when you get bogged down in sort of the day-to-day work—me, for example, as a marketing guy and as an analytics guy, I could easily spend all day every single day just running numbers on our campaigns. You know, digesting that data, making strategic pivots based on the data and letting the executives know what’s going on.

But if I do that all day every day, then we don’t move forward as a company. We don’t get projects done like introducing new features or marketing a new—like, we released Ontrapages, which is like a little brother product to Ontraport. And if we don’t make time for that kind of work, then it doesn’t happen. So we actually set aside three hours a day for every single employee to work on that kind of stuff.

Kirill: Yeah, totally. And I know from my own experience that you can get bogged down in this “business as usual” type of stuff and completely forget about business growth. So it’s a very innovative approach to telling everybody, “Hey, guys, we can’t talk for three hours a day. Just get the work done.” And it works, right? Ontraport is growing exponentially.

Sam: Yeah, yeah. We’ve been very fortunate. But the timeblock—we call it timeblock—but the timeblock schedule of those hours with no communication, just work on these big projects has been really great. Another thing that’s sort of a hidden benefit, and I’m sorry, I know we’ve talked a lot about management and operations, which is not the point here—and you can edit all this out, if you want—but one of the big benefits, one of the sort of hidden benefits of that system is that you find opportunities to empower your people to take on big projects as project leaders. So currently my team has two big projects we’re working on, and one of them is being led by one of our copywriters, and the other one is being led by our assistant marketing manager. And this is the first time for both of them leading a big whole-team project. So it’s a great opportunity, a great learning experience for them to take on these big projects and learn project management and learn delegation and learn all of these skills that they’re going to need in order to advance their careers, whether it be here or anywhere else.

Kirill: Yeah, I totally agree. Alright, so thank you for that overview. We’re going to now proceed to the data side of things to not disappoint our listeners. Alright, so tell us a little bit about data in your role. How do you use data as the marketing manager? Oh and the way I got to invite you on the podcast was I talked to Rochelle, because she does a lot of the product side of things, and I was asking her if maybe she wanted to come on the podcast, maybe she knew somebody, and she was like right away, “Sam is your man. You should go talk to Sam. He’s the man to talk to about data.” So tell us why everybody thinks you’re the person to talk to about data at Ontraport.

Sam: Why people think I’m the data guy?

Kirill: Yeah.

Sam: (Laughs) So I will tell you just sort of upfront here, my data experience in my day-to-day work with data almost exclusively occurs on the marketing side of our business. The reason I say that is there’s obviously a huge amount of value in data science and analytics on the user side of things, like analysing how our customers are using our application because we are a software company, and I don’t have a lot of insight into that. So I’m just going to preface this whole thing by saying that.

But for the marketing side of things, which is what I do do, it kind of goes without saying, but the truth is if you don’t know your numbers you don’t know anything when it comes to marketing. If I’m spending $100 on a marketing campaign and I don’t have any data on the backend of that campaign on how well it performed—and mind you, I’m talking about realistic data, not just like, “Oh, look it got this many clicks on Facebook or whatever,” but real data to drive our business forward—if I don’t know the results of that campaign, I have no idea if we should do it again, or if we should turn that $100 into $200. And that’s the part that I’m ultimately responsible for.

Kirill: Okay, gotcha. That’s a good comment, that user data is not what you work with. You’re specifically focused on the marketing data. As I imagine, the way current modern marketing works, you’d be like bombarded with data from all over the place. Where are these data elements coming from that you analyse?
Sam: Yeah, so we have a bunch of different tools and sort of entry ports for our data. I guess to say that I don’t run data on our users is partially true and partially false. The reason I say it’s partially true is because I don’t actually look at or use data about our clients, about our Ontraport clients all that often unless it’s presented to me and I’m asked to do something with it. But I do do a lot of data on our leads and on people who haven’t quite converted into a customer yet. So we have a lead management tool called Ontraport. It’s pretty awesome.

Kirill: (Laughs) Fantastic! Love the plug. Yeah, awesome.
Sam: (Laughs) Yeah, that’s where a lot of our data starts. So I have a lot of data on our leads and our lead health and our lead scoring and things like that. It all comes from our own application. I mean, yeah, obviously I’m going to plug Ontraport, but the truth is I’m sure that most entrepreneurs and business owners listening to this podcast have some tool that they’re using, whether it be CRM or auto-responders or whatever, that provides them that data. So that’s a big chunk of data that I look out on a daily basis.

In addition to that, I would say that next largest section of data that I look at is going to be from our advertising efforts. We do a lot of advertising on things like Facebook, Google AdWords, you know, re-targeting around the web. And I have a lot of different dashboards and data points coming at me from those platforms because really, that’s where we’re actually putting the money on the line. So I spend a lot of time with that data because I need to make sure that if I’m going to spend $1,000 on this advertising campaign, that I’m getting the backend that I need in order to justify that cost. So I spend a lot of time with advertising data.

And finally, the last big one that I look at a lot is going to be site engagement stuff. You know, this may be very basic for your podcast, but I do spend a lot of time in Google Analytics looking at how much time our people are spending on our pages, what they’re clicking on. We do some heat-mapping, you know, we look at that kind of stuff as well. So those are some of the three big buckets that I spend most of my time on.

Kirill: Okay, gotcha. So then the natural question would be from there, since you’ve got all these different sources of data—you’ve got the Ontraport system, which is very cool that Ontraport uses their own system in their business. That’s a great way to lead by example. And then you’ve got Google Analytics, you’ve got these heat maps, you’ve got all these sources of data. So the natural question from there is, how do you go about connecting this data? How do you go about joining these different datasets, understanding how to combine them into something that you can actually use?

Sam: Yeah, so that is actually a great question because that was—not to jump off track here for a second, but that was probably one of the biggest lessons I learned when I first got the role that I have now. My first entry into marketing here at Ontraport—because I wasn’t hired as a marketing guy, I was hired in 2009 to be support rep. That was 7 years ago now. So through the years I’ve grown into the marketing team, but my first role on the marketing team was actually data analyst. And if I’m going to be completely honest, the first thing I did as the new data analyst for Ontraport was Google “What does data analyst do?” (Laughs) “What do data analysts do? What is data analysis?” Things like that, right?

So one of the things I learned very quickly, one of the biggest challenges that I had to learn was how take all the data from all of the different sources and put it into one place that’s actually digestible, that’s actually usable. Because you’re absolutely right when you sort of alluded to here that you can get bogged down so easily and so quickly in overanalysing one element of your data, and that’s a huge mistake. Anyway, that’s sort of a preamble to set up that what I’ve done is looked at things that our software can do because we do have reporting metrics, obviously. Otherwise we wouldn’t be a piece of software worth owning. So we use our own software for as much as I can. Anything that I can’t use my own software for, I find myself using something like Wicked Reports, if you’re familiar with them. I use them for a lot of our sort of first click engagement reporting metrics. And what that does is it allows me to sort of take all those data points—because we’re talking about hundreds and hundreds of data points—and condense them down into what actually matters and then get streamlined reporting based on that. So Wicked Reports is a tool that we’ve just begun using that we’re really happy with.

But even if you’re not interested in Wicked Reports, one thing that we did very early on is we used UTM Variables, which are Google’s proprietary sort of tracking variables, and we slapped that on everything because what that allowed us to do was follow our efforts from not just “Did they convert on this ad or not?” but all the way through to “Did they ever purchase something from us? If so, how long were they a customer?” Things like that, right? Because that’s the data you don’t really think about and that’s the data that really matters. So what we would do is set ourselves up on the frontend with Wicked Reports, with UTM Variables, to be able to track that stuff all the way through. Then we created dashboards and metrics that help us track that kind of data. So to answer your question—I know I’m sort of like rambling here—but to go back to the original question of like how do you take all that data and turn it into something usable, the answer is I got rid of most of it as quickly as humanly possible because there was too much of it and I knew that if I showed Landon or Lena like a hundred pieces of data at the same time, it was not going to be useful. So the first thing I did was ditch as much of it as I could based on my actual objective.

Kirill: Gotcha. Very interesting. So kind of like get rid of all the stuff that you’re not going to be using and then start from a fresh page. Okay. And I really liked your comments as well about setting up these data points. So this is a very important part that a lot of businesses miss. They try to analyse data but they haven’t set themselves up for success. They don’t have the right data points. And I hope that our listeners will take this to heart. Like, you might have inherently some data points that already exist. Like, you might be measuring—alright, so you can tell how many people purchase because you can see how much money came in. Or you can tell how many people visited your website because you have Google Analytics telling you that.

But then to measure the journey of the customer through your website, seeing where they actually go from page to page, where they fall off, where they don’t click a button or which buttons actually get more clicks than others and so on. That doesn’t just come out of nowhere. You’ve got to set yourself up for that. You’ve got to put those data points in place. One of the examples that you mentioned—or two—was UTM Variables, which is Google’s proprietary thing, and Wicked Reports.

So tell us a bit more about Wicked Reports. Oh, by the way, guys, everybody listening to this, Ontrapalooza – great conference! I’m going to make this plug for you guys. Like, I was at Ontrapalooza this year. It’s a marketing conference in Santa Barbara. So if you live somewhere in California, it should be a no-brainer to go to Ontrapalooza. It’s usually in October. And it’s going to be the 6th one in 2017. If I flew in for Ontrapalooza from Australia—literally, I was in Santa Barbara for 4 days just for that and it was the best conference I ever attended.

Sam: Oh, thanks, man. That’s awesome.

Kirill: And you did a lot of the organizing there, right? Unfortunately I didn’t get to your presentations. There were so many people presenting at the same time. But yeah, the organization of that event was just incredible and the guest speakers that you had were also fantastic. And one of them was, I think, the founder of Wicked Reports. Is that right?

Sam: Yeah. I’m not sure if he was the founder or one of the founders or whatever his role was, but he’s a higher-up over there at Wicked Reports, yeah.

Kirill: Yeah. Again, I didn’t make it to that presentation. I was listening to Basic Bananas presenting on the customer journey or something. (Laughs) But what would you say about Wicked Reports? Like you said, you just started using it. And how does it compare to Google Analytics?

Sam: Yeah, you know, the simple—and I’m sure that if Andy—we talk to a guy over there named Andy. If he listens to this, he’ll probably want to slap me for missing huge selling points on the software. But the truth is for us it was actually a very simple choice to start checking them out because UTM Variables, if you’re not familiar—just a real quick summary of what they are—essentially what they are are variables that you add onto the end of a URL, and that information is then scraped by your whatever form of technology you’re using. Again, we’re using Ontraport. It will scrape that information and pop it into the contact record of the person who opted it. And what that does is it allows us to sort of track, “Okay, cool, so we know they came from this particular ad and this particular campaign,” and then we can tell if they purchased and stuff like that, which I was talking about earlier.
What Wicked Reports does is a little different. It allows us to not only track where they came from the first time because—and I should go back and mention, UTM Variables, I don’t know about everyone’s software, but for Ontraport it doesn’t overwrite. So if they opt-in from one ad, for an e-book or something like this, and they get all that data popped into their contact record from the UTM Variables, if they click on another ad a week later and opt-in from something else, it will not update those fields because UTM Variables are supposed to be original source. So those fields are stuck. But what that doesn’t tell me as the marketing guy is, “Okay, cool, yeah, he downloaded our e-book on email marketing or whatever. But then he downloaded an e-book on landing page optimization and actually ended up buying the next day from that.” And that’s a very interesting thing to know. And Wicked Reports allows you to sort of take that UTM Variable tracking because it’s very similar in the way it works, but it allows you to check based on first click, so that would be similar to UTM Variables, you know, the original source, as well as the last click before conversion. So that was a big selling point for us to check them out and try it out.

Kirill: Okay. Fantastic! That would be a real game changer to actually see what indeed triggers the customers’ final decision rather than what just brings them to your business.

Sam: Yeah, and that’s what it’s all about. And I know that was sort of a brief overview, but one of the areas that UTM Variables kind of fails at is—okay, great, I have an advertisement out there and I’m sure some of your customers are probably in our advertising group so they may have seen our ads for an e-book or whatever on an entrepreneurial topic. So they click on that, they fill up a form—great, that locks in to UTM Variables. I know that I generated a lead from that campaign. That’s awesome.

The problem is now we’re going to start sending out emails. We’re going to start nurturing them towards a purchase. We’re going to start teaching them stuff, we’re going to start pitching them stuff, whatever. And if they click on one of those emails and make a purchase, UTM Variables aren’t going to help me, right? Because sure, the UTM Variables and that e-book is what got them into our funnel in the first place, but which email in my funnel was the one that got them to purchase? And that’s a different conversation, right? And that’s something that Wicked Reports does really well.

Kirill: Fantastic. And at this point, this is where I like to mention that we’re not just talking about this as like evil marketers sitting here wearing our evil hats. All of this creates for a better customer experience as well, right?
Sam: Of course.

Kirill: On one hand you get to sell the product. But on the other hand, if you know from which email the customer purchased, like in this example, then you can follow up with the right content and give them the best experience after purchase. You know, that is what Ontraport, for example, is all about. It’s not about just selling and that’s it. It’s about having a great customer experience. You guys are going to have like a customer success team or something like that, right?

Sam: Yeah. Yeah, we have a whole team of people dedicated to helping new Ontraport customers sort of set up their accounts and make sure that they’re good to go. Because like you said, sure, I’m the marketing guy and my job is to be harsh about data and tell Landon and Lena, “Hey, guys, this ad works and we’re getting a ton of conversions,” which is kind of the opposite of touchy-feely. That said, we have tons of people here at Ontraport whose job it is to make people feel great and to have a really good customer experience and that’s the backend. You absolutely have to have that part. Because if all you’re worried about is conversion rates on all your emails, then you’re going to end up with emails that are spammy, and weird, and no one likes them. And that’s where you end up in that sort of evil marketing corner that you’re talking about.

Kirill: Yeah, exactly. Exactly. So, we know where you get the data from. We know how you go about putting it together. Alright, now you have the data. What do you do with it?

Sam: So what I do with it is a couple of things. The first thing I do is I analyze the data for my own purposes. And what I mean by that is the first thing I do with any set of data is I spend some time looking at it and sort of reflecting on it. I use my own brain and my own knowledge of what we’re trying to accomplish and I determine, based on my opinion, is this successful or not, what could be better, where are we failing, and things like that. And I do this with sort of raw data.

So I take all the data, I throw it into a big spreadsheet, or whatever it is I’m looking at, and I start to analyse it myself. I’m taking notes; I’m doing whatever I’ve got to do. Then what I do once I sort of made my conclusion about it is I will inform the team, sort of what’s going on and we’ll all look over the numbers together so that we can pivot if we need to, or we can take advantage of something that maybe they didn’t find in their first impressions of the data. So if there’s like some sort of a hidden gem like, “Oh, hey, guys, this is working really well. Let’s focus on this,” that kind of stuff, then finally I take that data and I turn it into something digestible for people who aren’t on my team. So, one big spreadsheet with like hundreds and hundreds of numbers on it is not digestible. I know I’ve talked about it a couple of times. I apologize.

Kirill: (Laughs) Whoa! You got me scared for a second. I thought you were going to say that’s what you create, that one big spreadsheet.

Sam: No, no, no, no. That’s what I start with. That’s what I like but that’s my job, is to like that, right? (Laughs) But I take that spreadsheet with the God-awful number of numbers on it and actually one thing I do is I bought—there’s a web app called Infogram. I think it’s like “Infogr.am.” It’s like 5 bucks a month or something like that, but what it does is it allows you to pop data in in like a spreadsheet format and then it will make like infographics out of it. So I actually do that with our data. I take the relevant numbers that I think prove whatever point it is that I’m trying to make. I throw it into something like that and then that’s what I send off to my boss, to the president and the CEO, so that they know where their business is headed. Now, if it was just me, I may not do that because I don’t mind the big spreadsheets, but that’s one thing that I do with it almost every time. So any time there’s like a big funnel—actually, I may do it later today. Me and Lena just tested a big funnel last week. I have all the numbers, I’ve crunched all the numbers, I’ve looked at all the data, I’m going to take the relevant information, put them into one of those Infogram infographics, and that’s what I’m going to bring to our meeting later to show her on a screen.

Kirill: Oh, fantastic! That’s a very efficient way of showing data. Because a lot of the time, data scientists either present these huge spreadsheets or they spend hours and hours and hours trying to create a visualization or trying to come up with a way to present this data. And you’ve just come up—or you’re using a very quick way. What was it, infogr.am? Is that right?

Sam: Yeah, I think it’s infogr.am. I’m pretty sure that’s the website. But if you just google “infogram” you’ll find it. But yeah, that was the thing. I did exactly what you’re talking about. My first time showing data, you know, when I was the data analyst googling how to do my job, I would show giant spreadsheets. And very quickly, I would look around the room, and I would notice that nobody was paying attention 20 minutes in. Because it’s just—it’s unbearable. If you don’t know what you’re looking at, it’s not usable.

So then I tried making visualizations like you’re talking about. I would spend a lot of time in Excel or Google sheets trying to make these graphs and it was just taking way too long. So my new problem was like, yeah, I was getting cool visualization, but I was spending way too much time doing it. So this thing, you know, it costs us almost nothing, and I think they even have a free version. So if you don’t care about their branding on your stuff or whatever, there is a free version. So I think I even used the free version. But I found that what I would do is they just have spreadsheets. That’s where they get the data to present these graphs. So I would just copy and paste entire sections of my spreadsheets into this thing and it would spit out really pretty visualizations that are interactive, you can mouse over all the points to see the exact numbers and all that kind of fancy stuff. And I just made it a lot easier on myself.

Kirill: Wow, man, you just got me excited about that. I’m going to check it out after this podcast. That’s fantastic.

Sam: Yeah, you should. Honestly, I think it’s like $5 or $10 a month but what it saves the company in my time is well worth that amount of money.

Kirill: Totally. And when you’re like presenting internally, within the company, not like as a consultant to your client, but internally, you don’t have to go that extra mile of making it look absolutely fantastic. You just want efficient presentation, right? Something that’s not raw numbers but something that is very visual. And if it looks like the colours are not ideal or the interactivity is not ideal or something like that, nobody cares about that as long as you get the point across, right?

Sam: Right. And also, they all have the context. I think that’s one thing for consultants, and if you do have any consultants listening, I want to make it clear: I wouldn’t recommend Infogram for like your next big presentation to a business. (Laughs) You should absolutely do the homework and get the really impressive presentations, spend the time. But like you’re saying, for internal presentations, they know the context. They know what the campaign was, they know what we were trying to accomplish. They know what a CTR is. They know what a CPC is. I don’t have to explain all of this information on different slides or whatever. I just need to show them the graph with the numbers on the graph that tell them whether or not it’s working and that’s it. And the fewer slides, the better. Really! Or the fewer infographics I have to use, the better. Like I said, the first thing—and I’m sure there are people who are much more educated in this arena than I am listening who are probably very angry with me—but the first thing that I do with any amount of data is I get rid of as much of it as I possibly can. I have to find the numbers that matter the most, what is our objective, are we achieving that objective, and I work from there.

Kirill: Yeah, gotcha. That was a very good overview of presenting, of actually being that bridge between the analytics and the stakeholder. So I’m sure that a lot of our listeners will get some takeaways from there. And the next thing I wanted to talk to you about is like—rewind a little bit back. So you’re doing the analytics. You’ve got the data, you’re doing the analytics. But before you present, what techniques do you use? What are the most common techniques you use in your analytics when you’re working with marketing data?

Sam: I’ll tell you the big one. The big one that I use, and this was something that—again, I know I’ve said this a couple of times but I want to make it very, very clear. I have no formal education in this, so I learned a lot as I went. I was put into a role that I was absolutely not qualified to do, and I sort of learned along the way. And one of the big lessons I learned, in addition to how people simplify data and all that stuff, was this concept of statistical significance. And especially in marketing, where you do a lot of testing and a lot of split testing and A/B testing and multivariate testing and blah-blah-blah-blah, it’s so, so, so important that you know whether or not your results are statistically significant.

And what I mean by that is, if you’re not familiar with the term, basically, very smart mathematicians out there in the world have developed these equations that will tell you how confident you can be in the results that you’re seeing, and more importantly in the difference in those results. So let’s take an example. Let’s say I have two ads running on the same thing, and the only difference is the image on those two ads. I’ve got a Version 1 and a Version 2. If I have a higher click-through rate on Version 1 than I have on Version 2, initially your thought would be, “Great, Version 1 wins. Let’s kill Version 2 and keep moving forward with Version 1.”

The problem is you can’t be sure on a smaller scale if that will continue to be the case, that Version 1 will continue to win. So let’s put some more context on that example. Let’s say that those ads have only been shown to 200 people each. Your likelihood of those numbers repeating and it not being just chance is pretty low. So you have to know whether or not you can move forward confidently knowing that those results will hold. So that was a big one that I learned very, very early on.

And then to answer your question more specifically, what techniques and tools do I use, I use a calculator to tell me whether or not my results are significant. I don’t present anything to the executives or to even my team unless we have significant results. Because what’s the point, right? If I show the CEO—going back to our earlier example—if I show him the results of Version 1 and Version 2, he’s probably going to tell me to turn off Version 2 because Version 1 is winning but boy, that could be wrong. They call that a false positive. You have to make sure that your numbers are correct. So the calculator that I use—I used ones that I just found on Google or whatever for a long time, but the challenge that I ran into with those was that they only allowed, most of them at least – I know you said you found one, before we started recording you were telling me about one. I had never found one, but they only allowed you to compare two things. So 1 vs. 2 or A vs. B.

The problem I was having was—like I said, a lot of what we do with advertising and, you know—we’ll launch 25 ads just to test, copy and image and whatever, but they all go to the same place. So what I have do is very quickly run statistical significance calculations on all 25 of those ads. And the problem with the 1 to 2 comparison, because I’m sure some of you are like, “Okay, just fill the thing out 10 times, 2 ads at time. That’s 20 times. It’s not that big of a deal.” The problem is what I didn’t realize—and again, I learned as I went—but one thing that I learned very quickly is it’s not 1 to 2, 3 to 4, 5 to 6. It’s 1 to 2, 1 to 3, 1 to 4, 1 to 5 – all the way through. And then 2 to 3, 2 to 4, 2 to 5. I mean, there’s hundreds and hundreds of comparisons. And we used to do it by hand and it took us hours and hours and hours.

So I put together a Google Doc, it’s called the Stat Significance Calculator. And I basically just took the formulas that I found online on how to generate these kinds of numbers, slapped it into a spreadsheet, put some styling on it and made is as simple as I could to save us time. Before we started recording I gave you a link to it—I don’t know where you’re going to put it—but you guys are welcome to make copies of this. I made a copy of it, then I made it public. So you guys can have this. Just go to the link, copy it and then it’ll copy it to your Google Drive and you can use it. But this thing saves me tons and tons and tons of time every single day.

Kirill: Yeah, thank you so much for that. Sam did share his tool with us and we’re going to put it on the show notes so make sure to check that out and download it. And if this is something that Ontraport use in their business, and you either have a business or you’re in marketing, then this is something you should be using as well. So thank you so much for sharing that.

Sam: No problem. And the one thing I will mention—sort of the two things I will mention. The great thing about this calculator is—and I want to make this very clear, I did not invent Statistical Significance. I take no credit whatsoever. I just found the equation online and put it into an easy-to-use template. So, you know, all the credit goes to everyone else. But anyway, one of the things that’s beautiful about this calculator is it takes a lot of the emotion out of the equation, so you no longer have to say, “Here are the results,” and then have an argument about what they mean. This thing will help you make your determination about what’s winning and what’s not, emotions aside. The other thing I will mention really quickly, because if you are familiar with this concept, you’re probably familiar with the idea that you should run on a confidence level of about 90-95% confidence that your results will continue, you know, significance. We believe that as well. That said, the calculator that I have developed here is running on an 80% confidence, so if you want to tweak that, you can dive into the formulas and find how to up that. But it is running on 80% so just know that. I feel like I should put a disclaimer on here. (Laughs)

Kirill: Yeah, totally. And I’ll add a disclaimer as well. Our listeners, if you guys decide to download this tool, then just know that you’re doing it at your own risk. Ontraport do use this tool, but Sam and Ontraport have no liability for this tool, so just double-check everything that you’re using and make sure you’re comfortable with this tool before you proceed to actually base your business decisions on it.

Sam: Yes, definitely do that. In fact, I would recommend just to take that one step further. I would recommend that you do this action manually a couple of times just to familiarize yourself with how this even works and then start using an automated tool like this. That’s sort of a philosophy we have around here. Ironically, we’re an automation company, but we always try to do something manually first and then we automate it. So if something breaks or if you’re questioning whether or not this thing is actually working, you can go double-check your math and do it yourself. So I highly, highly recommend that. And like you said, I take no responsibility because this is way above my head. I’m not even entirely sure how it works, to be completely honest with you. (Laughs) I just know that that’s what I read so I threw it in a doc.

Kirill: Okay. Thank you so much. So that was a great overview of statistical significance and I cannot stress enough how many times, how often it happens that marketers, and even people not in marketing, underestimate the importance of statistical significance. Just imagine that like, pretty much all of the drugs that you get from the pharmacy, they have undergone tests. And all those tests—they’re like either A/B tests, you’ve probably heard the terms “control group” and “placebo effect”, where somebody gets a placebo pill and somebody gets a normal pill. And all of that says, “Well, guess what? Statistical significance is also used to prove that drugs are working and things like that.” So it’s a very, very important concept and Sam gave a very good outline of what it is and how it works and why it’s important to run those tests before you present your results. So I can’t add anything to it except for just make sure to use statistical significance and keep it in mind in your analytics. Moving on from techniques—we’ve talked about presenting, we’ve talked about techniques, we’ve talked about the way you get data, how you put it together.

The next thing I want to talk about is quite interesting because you mentioned a couple of times that you don’t have any formal education in this space. Actually, I’m going to share a little surprise. You probably didn’t know this, I found your video from Ontraport, the careers video. (Laughs)

Sam: Oh God. (Laughs)

Kirill: On YouTube. So if anybody wants to see Sam when he was a bit younger, just go to YouTube and find that video. But basically, there the main thing related to data that you mention is that you type really fast, which is a great example for a lot of our listeners. So the question is, how do you go from not having any formal education in the space of data, and just typing really fast, to being a marketing expert and leading a team of eight people in data science presenting business decisions or things that drive business decisions to the president and the CEO of a company. What has been your journey and what have been like the main points that you can outline to us in that journey?

Sam: Okay. Well, I guess to start, you know, I think it helps a lot that I grew up with an interest in technology and software and in this kind of stuff. To be completely honest with you, my hobbies and my interests are all entirely nerdy. So I just happen to enjoy this kind of work, which I think is a big part of why I’m successful at it.
But as far as my journey goes, you know, like I mentioned earlier, I was hired in 2009 as the seventh employee and I was hired to be a support rep, so to answer the phones and help clients. And that was great because our business serves businesses. Our clients are business owners, so when I was helping clients, I was getting a really good indoctrination and a really good education on how business is done in the real world, how people are setting up their businesses, how people are setting up their tools like Ontraport to automate their businesses and run their businesses. So that was really a great starting point, was just learning how business is done, learning what lead magnets, learning what landing pages are, learning what an email funnel is, things like that. So I spent a couple of years learning that kind of stuff.

Then the next step for me here at Ontraport was I moved on to our services team with Rochelle, who I know you’ve mentioned you met, and me and Rochelle built our services team. And what that was at the time was you could call in and pay us and we would build stuff for you in your Ontraport account to run your business. So that was great because that gave me the opportunity to hear Rochelle interact with people—who was much more knowledgeable at the time than I was—about how marketing works and how business works. So I was able to listen to her, get a good education from her and then I would take people’s businesses and their strategies and I would help them implement them. So now I’ve got some general business knowledge and some hands-on building systems and processes knowledge, which was great.

From there, once we sort of built the services team out, I was actually very fortunate and I was able to help—I don’t know if I can say their names or not, so I’m just not going to—but we had three or four big, big clients that needed help with product launches. And they needed help with marketing and with actually carrying out their launch. Let me rephrase that. They didn’t need help with marketing. These people are obviously very successful. I know they know how to market. But they needed my help with implementing their marketing and the tracking, the reporting metrics on the Ontraport side so that they can tell whether or not it was working.

And that was great. That was probably the best education I got because I got to see how were the big boys doing marketing, how were the big boys reporting on that marketing. So I was actually a part of, a very integral part of, multiple launches that did well over a million dollars in sales. And that was great. That was probably the tipping point for me.

After that, I was given the opportunity to help market our own brand. So yeah, that’s sort of my journey, I guess. But it was a little more than that. I spent a lot of time on nights and weekends—especially after I got the first data analyst job, like I said—I spent a lot of time on nights and weekends reading blogs and googling and reading books and just anything I could put my hands on to try to become educated and it set myself up for success.
Because, you know, while we’re sitting here and we’re talking about it on a podcast and it’s fun to pretend like I’m the king of the mountain or whatever, and I know what I’m doing, the truth is at the time I just wanted a successful career. I just wanted to be successful in my own career personally. I wanted to get promotions, I wanted to get raises and I wanted to be valuable to the company so I applied my learning to that field. You know, that’s what I did.

Kirill: Yeah. That’s a great journey that you’ve had and learning through experience, you’ve had some fantastic opportunities. Also, it’s a big challenge to stand up to something that you don’t know at all, especially like going into million dollar projects or million dollar launches and assist with that. That’s a big thing to do. And it’s great to hear that you went through all of that and successfully came out on the other side.

Sam: Well, that is one thing. It’s a great opportunity. And again, I don’t want to derail us and talk about people management, but it just so happens to be something that I’m learning myself, and I want to impart on your people because I’m sure you have people who are business owners themselves maybe looking to hire people. But the truth is, you know, one of the biggest things I learned about being an effective employee, and especially because I know I saw in your notes before we hopped on the call here that it sounded like some of the people who listen are looking to get careers going in this field.

Kirill: (Laughs) More like 80% of the people.

Sam: Okay, great. That’s actually great, because I will tell you the one bit of advice that I give to all of my people as I hire new people and whatever is, “Never say no to a challenge.” And I know that that sounds like something that you put on a motivational poster and stick up on the calendar or whatever but the truth is, one thing that led to my success in this field and really all of my team’s success and anyone I know who is successful is—you know, when we were a lot smaller, Landon would come out of his office and he’d say, “Hey, I need someone to do a project,” and my hand was always the first one up. You have to learn by doing.

Reading the books, reading the blog posts, listening to this podcast – while it’s a great first step, it will not replace the experience of actually doing something and putting your money on the line and putting your reputation on the line. Because when your back is against the wall, that’s when you do the most learning, right? When I had a phone call with one of our clients and he said, “Hey, man, this launch is about to go off. I’m expecting millions and millions of dollars. Are you sure this thing works?” That was a—I had to make sure that it is, right? I didn’t have the option of just like, “Well, I read it in the book, so I’ll put that in my memory and hopefully work on it one day.” Do the work. Do the actual work and that’s the best advice I can give anybody looking for a career in this field or really any field.

Kirill: Yeah. Fantastic. That is exactly the question I was about to ask, what advice you can give, and that’s a perfect answer.

Sam: Oh, great. There you go. (Laughs)

Kirill: Never say no to a challenge. There you go. Especially in data science.

Sam: Never say no to a challenge. And, you know, the other thing—and this is something that I’m working with people on my team—the other thing, especially with analytics and with marketing, is you are going to find yourself in a situation where you do not know the answer more often than not, if you’re coming from an non-educated perspective like I am. You know, if Landon asks me for something tomorrow that I don’t know the answer to, I don’t have the option of saying, “Sorry, man. I don’t know.” I have to then go figure it out. And that’s something that I feel like a lot of people think they’re really good at but may actually not be great at. You have to be willing to do the research, learn how to do things on the fly as you run into challenges.

I know a lot of people in our engineering team who became engineers and developers by just doing exactly that. They would come up with a concept. They’d be like, “Hey, I want to make an app that does this.” And then they’d be like, “Okay, well, what’s the first thing I need to know?” And they would google it, and then they would do that, and then they would google the next thing and they would google the next thing and sure enough, after months and months of hard work and research, they had something that worked. And then you’re able to retain all that information and build on it. So, yeah, don’t turn down any projects and be willing to be unstoppable, which is actually one of our values painted on the wall here. Be unstoppable. Learn the thing that you need to learn in order to do the job.

Kirill: Thank you. Very inspiring. Just some great advice, and hopefully everybody takes it to heart. And my next question is what is—like, building on top of that, it’s a very inspiring and passionate thing to not say no to a challenge, and especially in data science, and figure things out. What would you say is your most favourite thing about being a data scientist?

Sam: Oh, man. My most favourite thing about being in data science or working in analytics? I will tell you one of my favourite things was it really taught me how to deal with being wrong, which is something that I don’t think many people are good at, myself included. You know, we all tend to think that we’re right 99% of the time, and nothing will set you straight, as far as being wrong about something, like data will.

I spent a lot of time when I first started doing this job—a lot of time coming up with really great ideas, fleshing them out perfectly, executing them only to find out that I was 100% incorrect and it was a total failure. And it’s weird to say that that’s my favourite part, but it actually is. Because only in marketing—well, that’s probably not fair—but marketing especially is one area of business, or life in general, where you have the tools and the ability to prove a theory without a doubt, especially if you use statistical significance, but without a doubt, you know whether or not something works. There is no ambiguity, there is no emotion, there’s no arguing. It’s just, “Did it work? Yes or no?” And that is so powerful, to be able to say, “This was the strategy we tried. It didn’t work. Where do we go from here?” I love that part. That’s probably my favourite part.

Kirill: Fantastic. Great. Great overview. So moving on to kind of like the overall view of where you’re sitting and from what you know about data. It was interesting how you corrected the question for the previous one where I said, “What’s your most favourite thing about being a data scientist?” and you said, “Being in data science or working analytics,” which is fair because your title probably isn’t directly data science, but the stuff you do is on a data science level.

And hence my next question is, from what you see about the world and what you’ve already experienced with data in your role and in your life, where do you think the field of data science and just data analytics is going? What do you think our listeners should prepare for in the future to be ready for a career in data?

Sam: That’s a great question. I think that—and maybe my opinion is a little biased here because I work for a software company that provides data, but I think that software nowadays, especially in the business and marketing space, which is the space that I work in primarily, every bit of software that you could possibly get for your business and for marketing is going to include data analysis or metrics dashboards, or whatever their version of that is, in their tool. And what anyone who’s listening here can do to elevate beyond that, because what that’s going to do is a lot of people are going to think, “Okay, cool, I’ve got the data. Why would I hire a data scientist? Why would I hire a data analyst when I have a tool that tells me what I need to know?”

But what I’m finding in my research and in using tools and in our own product development is that there is a whole another level to data science and to analytics and into metrics that will not be covered by basic software solutions. And the best thing that you can do as an aspiring data scientist is learn what the difference is, learn what they’re not providing. And by the way—little spoiler alert—most of them aren’t providing statistical significance, at least not yet, so that’s a great way to start. I mean, I know that our tool and none of our competitors’ tools do, so that’s a great place to start if you’re trying to prove value as a data and marketing analyst.

But learn the difference, right? Learn the difference between the basic data that they’re providing and the data that you can provide as someone trained in this field, and that is how you become invaluable to a company, is being able to take the data that they provide and take it to the next level and provide extra value to your employer or to your own business or whatever.

Kirill: Fantastic! That is a great summary. So, basically look into things that do not come pre-packaged with the solutions that are offered on the market place or in the target company that you want to have a career in. Or maybe you already have a career in that company and just look around and see what else you can add to enhance that data and those decisions that are being made.

Sam: Well, I’ll tell you right now, especially because you said like 80% of your audience are people looking for jobs in this field. I’ve got a meeting with Lena in like an hour or whatever, and if I go in there and she fires me, the very first thing I’m going to do is I’m going to find software companies or whatever field I want to work in, and I’m probably going to try to apply as a marketing person and as a data person. What I’m going to do is I’m going to check out their websites, I’m going to dig around in the source code of their websites, find what tools they’re using, and then do some research on what data and metrics those tools are providing, and then I’m going to take it a step further and that’s what I’m going to bring in my interview. To be completely honest, that’s exactly what I’m going to do.

Kirill: Fantastic! Well, let’s hope Lena doesn’t fire you. (Laughs)

Sam: Yeah, let’s hope. (Laughs)

Kirill: Yeah, that is a great tip. Yeah, there you go. If somebody listening to this podcast specifically wants to get into data science and marketing, that is golden advice. Just go to the website of a company that you have an interview with, right-click on the website in Chrome, select “view source code” and go through their source code and find out what tools they use for their marketing and see how you can add value. I bet nobody else in the interview is going to do that.

Sam: Nobody else is going to. And in fact, I’ll even give you a tool that helps a lot with that. There’s a website called builtwith.com. If you check that out, you pop in the URL of any page, you can see what technologies they’re using so that makes it even easier for you. So that’s what I would do. I think they charge for something but you can do that searching stuff for free. So I would do that. In fact, I know a consultant of ours—we certify people to be consultants for our product—and I know a consultant of ours, I was just talking with him the other week, and he was telling me that this is actually how he built his business. He would go to the website builtwith.com, find businesses in his area that were using Ontraport, or using a competitor of Ontraport, and then put together like a pitch and then e-mail them and say like, “Hey, this is what you’re using. This is where you’re going wrong. This is what I can help you do. What do you think?” And he built a very successful business doing that. So if I was looking for a job, that’s the first thing I would do. Yeah.

Kirill: Fantastic! Thank you very much. Builtwith.com – guys, check it out. All right, thank you, Sam. We’re nearing the end. It was a great pleasure having you on board on this podcast. Going forward, how can our listeners contact you, follow you or find you on the Internet so they can learn more about your career?

Sam: Boy, that’s a good question. And it’s funny, ironically for a marketing guy, I don’t really use social media all that much. I mean, I have a Facebook page. You’re welcome to find me on Facebook and add me, but I don’t post anything insightful about data. I just talk about video games and comic books and stuff. So that might not be helpful. If you do have any questions or you are interested in what I’m up to, I do write for our blog which you can find on our website or just go to ontraport.com/blog. I occasionally will write a blog post there. I’m actually working on one right now. So I will talk about marketing and analytics and stuff like that on there. And if you have any questions about anything, my e-mail address is [email protected] Feel free to e-mail me. Don’t get mad at me if I don’t respond right away because I actually do have a job that I have to do, but I’m happy to share whatever I know.

Kirill: Fantastic! Thank you very much. And we’ll share those links on the show notes, including the blog post, so hopefully by the time this podcast is out your blog post will be ready and we’ll get people checking it out. And one final question: What would you say is your one favourite book that you can recommend to our listeners for them to become more data savvy or just improve their life in general?

Sam: So I actually have a couple different answers to this question. The book answer, the real tangible book answer would be a book called “Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die.” It’s written by Eric Siegel. You can find it on Amazon or whatever. That’s a great book. So I love that. It’s kind of an introductory book to the concept of predictive analytics, which is what I deal with a lot. So I encourage you to check that out if you’re looking for a book.

That said, I love reading blog posts. That’s what I spend a lot of my time reading. I love Kissmetrics blog. If you’re not familiar with them, I think it’s just kissmetrics.com. They’ve got a great blog for data and analytics and stuff like that. I don’t actually use their tool, so I can’t speak to the quality of the tool, but I know their blog is great. So check out their blog.

And then I have a really niche one, but I would just be doing you a disservice if I didn’t tell you. If you’re doing Facebook ads, if you’re into Facebook ads and you’re running numbers on Facebook ads specifically, check out a guy named Jon Loomer – that’s J-O-N-L-O-O-M-E-R, and his website is just jonloomer.com. The guy has a killer blog, great advice on how to run numbers on that stuff and how to improve your Facebook ad campaigns based on data. I highly, highly recommend that. I spend a lot of time reading that guy’s blog. So those are sort of three sources that I like a lot.

Kirill: Fantastic! Thank you. So I’ll just summarize those. We’ve got “Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die”. I forgot the author’s name but we’ll put that in the show notes.

Kirill: Okay. So the blog Kissmetrics, and we’ve got another blog – jonloomer.com. So check those out if you want to propel your career in data science in marketing. Thank you very much, Sam, for coming on the show. It was a great pleasure to have you.

Sam: Thank you so much for having me. It’s an honour. This is my first podcast appearance. (Laughs)

Kirill: All right. So we’ll definitely share everything with our listeners. Get ready to be bombarded with e-mails and contact requests.

Sam: (Laughs) Yeah, I know. I kind of realized after I said that that maybe I shouldn’t have given my email but screw it. (Laughs)

Kirill: All right. Take care, my friend. Thank you so much.

Sam: Okay. Thank you so much.

Kirill: So there you have it. I hope you enjoyed today’s show. And my personal favourite part was when Sam discussed how he goes about taking data that he gets, which is lots and lots of data, and only leaving the data that is indeed necessary for his analysis. So to speak, cutting down the data. And part of that is of course setting up the data points in advance so that he only gets the relevant data. But even when he gets a lot more irrelevant data, he knows how to go about cutting it down. So that’s a very important skill to have, preparing the right data for your analysis so you don’t get overwhelmed and lost in all that data.

And of course, it was so great of Sam to share his tool which he uses for testing for statistical significance. We’ve got that tool prepared for you so if you want to download it, come on over to www.superdatascience.com/23. You can get it there plus you can get the show notes and the transcript for this episode and all of the links to all of the resources and materials mentioned here. So we extend a huge thank you to Sam for being on the show and to Ontraport.

And finally, if you’re a small business owner or a fellow entrepreneur, I highly recommend for you to check out Ontraport. They can really make your life much easier with their business automation software. So head on over to www.ontraport.com and check out what they have there to offer you. And on that note we’re going to wrap up today’s episode. I can’t wait to see you next time. And until then, happy analyzing.

Kirill Eremenko

I’m a Data Scientist and Entrepreneur. I also teach Data Science Online and host the SDS podcast where I interview some of the most inspiring Data Scientists from all around the world. I am passionate about bringing Data Science and Analytics to the world!